Ancestry-Dependent Enrichment of Deleterious Homozygotes in Runs of Homozygosity.

Runs of homozygosity (ROH) are important genomic features that manifest when an individual inherits two haplotypes that are identical by descent. Their length distributions are informative about population history, and their genomic locations are useful for mapping recessive loci contributing to both Mendelian and complex disease risk. We have previously shown that ROH, and especially long ROH that are likely the result of recent parental relatedness, are enriched for homozygous deleterious coding variation in a worldwide sample of outbred individuals. However, the distribution of ROH in admixed populations and their relationship to deleterious homozygous genotypes is understudied. Here we analyze whole-genome sequencing data from 1,441 unrelated individuals from self-identified African American, Puerto Rican, and Mexican American populations. These populations are three-way admixed between European, African, and Native American ancestries and provide an opportunity to study the distribution of deleterious alleles partitioned by local ancestry and ROH. We re-capitulate previous findings that long ROH are enriched for deleterious variation genome-wide. We then partition by local ancestry and show that deleterious homozygotes arise at a higher rate when ROH overlap African ancestry segments than when they overlap European or Native American ancestry segments of the genome. These results suggest that, while ROH on any haplotype background are associated with an inflation of deleterious homozygous variation, African haplotype backgrounds may play a particularly important role in the genetic architecture of complex diseases for admixed individuals, highlighting the need for further study of these populations

454-Pyrosequencing: A Molecular Battiscope for Freshwater Viral Ecology

Viruses, the most abundant biological entities on the planet, are capable of infecting organisms from all three branches of life, although the majority infect bacteria where the greatest degree of cellular diversity lies. However, the characterization and assessment of viral diversity in natural environments is only beginning to become a possibility. Through the development of a novel technique for the harvest of viral DNA and the application of 454 pyrosequencing, a snapshot of the diversity of the DNA viruses harvested from a standing pond on a cattle farm has been obtained. A high abundance of viral genotypes (785) were present within the virome. The absolute numbers of lambdoid and Shiga toxin (Stx) encoding phages detected suggested that the depth of sequencing had enabled recovery of only ca. 8% of the total virus population, numbers that agreed within less than an order of magnitude with predictions made by rarefaction analysis. The most abundant viral genotypes in the pond were bacteriophages (93.7%). The predominant viral genotypes infecting higher life forms found in association with the farm were pathogens that cause disease in cattle and humans, e.g. members of the Herpesviridae. The techniques and analysis described here provide a fresh approach to the monitoring of viral populations in the aquatic environment, with the potential to become integral to the development of risk analysis tools for monitoring the dissemination of viral agents of animal, plant and human diseases

Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance

Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are uncertain processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data, and introduce entropy-based loss terms. Experiments in image compression and image classification on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve better test metrics.Comment: 13 pages, 4 figure

Inner wellbeing: concept and validation of a new approach to subjective perceptions of wellbeing-India

© The Author(s) 2013. This article is published with open access at Springerlink.com. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.This paper describes the conceptual development of a multi-domain, psychosocial model of 'Inner Wellbeing' (IWB) and assesses the construct validity of the scale designed to measure it. IWB expresses what people think and feel they are able to be and do. Drawing together scholarship in wellbeing and international development it is grounded in field research in marginalised, rural communities in the global South. Results from research in India at two points in time (2011 and 2013) are reported. At Time 1 (n = 287), we were unable to confirm an eight-factor, correlated model as distinct yet interrelated domains. However, at Time 2 (n = 335), we were able to confirm a revised, seven-factor correlated model with economic confidence, agency and participation, social connections, close relationships, physical and mental health, competence and self-worth, and values and meaning (five items per domain) as distinct yet interrelated domains. In particular, at Time 2, a seven-factor, correlated model provided a significantly better fit to the data than did a one-factor model.This work is supported by the Economic and Social Research Council/Department for International Development Joint Scheme for Research on International Development (Poverty Alleviation) grant number RES-167-25-0507 ES/H033769/1. Special thanks are due to Chaupal and Gangaram Paikra, Pritam Das, Usha Kujur, Kanti Minjh, Susanna Siddiqui, and Dinesh Tirkey

Comparison of Tracking-By-Detection Algorithms for Real-Time Satellite Component Tracking

With space becoming more and more crowded, there is a growing demand for increasing satellite lifetimes and performing on-orbit servicing (OOS) at a scale that calls for autonomous missions. Many such missions would require chaser satellites to autonomously execute safe and effective flightpath to dock with a non-cooperative target satellite on orbit. Performing this autonomously requires the chaser to be aware of hazards to route around and safe capture points through time, i.e., by first identifying and tracking key components of the target satellite. State-of-the-art object detection algorithms are effective at detecting such objects on a frame-by-frame basis. However, implementing them on a real-time video feed often results in poor performance at tracking objects over time, making errors which could be easily corrected by rejecting non-physical predictions or by exploiting temporal patterns. On the other hand, dedicated object tracking algorithms can be far too computationally expensive for spaceflight computers. Considering this, the paradigm of tracking-by-detection works by incorporating patterns of prior-frame detections and the corresponding physics in tandem with a base object detector. This paper focuses on comparing the performance of object tracking-by-detection algorithms with a YOLOv8 base object detector: namely, BoTSORT and ByteTrack. These algorithms are hardware-in-the-loop tested for autonomous spacecraft component detection for a simulated tumbling target satellite. This will emulate mission conditions, including motion and lighting, with a focus on operating under spaceflight computational and power limitations, providing an experimental comparison of performance. Results demonstrate lightweight tracking-by-detection can improve the reliability of autonomous vision-based navigation

Scoping Review of Distribution Models for Selected \u3ci\u3eAmblyomma\u3c/i\u3e Ticks and Rickettsial Group Pathogens

The rising prevalence of tick-borne diseases in humans in recent decades has called attention to the need for more information on geographic risk for public health planning. Species distribution models (SDMs) are an increasingly utilized method of constructing potential geographic ranges. There are many knowledge gaps in our understanding of risk of exposure to tick-borne pathogens, particularly for those in the rickettsial group. Here, we conducted a systematic scoping review of the SDM literature for rickettsial pathogens and tick vectors in the genus Amblyomma. Of the 174 reviewed articles, only 24 studies used SDMs to estimate the potential extent of vector and/or pathogen ranges. The majority of studies (79%) estimated only tick distributions using vector presence as a proxy for pathogen exposure. Studies were conducted at different scales and across multiple continents. Few studies undertook original data collection, and SDMs were mostly built with presence-only datasets from public database or surveillance sources. The reliance on existing data sources, using ticks as a proxy for disease risk, may simply reflect a lag in new data acquisition and a thorough understanding of the tick-pathogen ecology involved

Fabrication of Electrochemical-DNA Biosensors for the Reagentless Detection of Nucleic Acids, Proteins and Small Molecules

As medicine is currently practiced, doctors send specimens to a central laboratory for testing and thus must wait hours or days to receive the results. Many patients would be better served by rapid, bedside tests. To this end our laboratory and others have developed a versatile, reagentless biosensor platform that supports the quantitative, reagentless, electrochemical detection of nucleic acids (DNA, RNA), proteins (including antibodies) and small molecules analytes directly in unprocessed clinical and environmental samples. In this video, we demonstrate the preparation and use of several biosensors in this "E-DNA" class. In particular, we fabricate and demonstrate sensors for the detection of a target DNA sequence in a polymerase chain reaction mixture, an HIV-specific antibody and the drug cocaine. The preparation procedure requires only three hours of hands-on effort followed by an overnight incubation, and their use requires only minutes